DCAMamba: Mamba-based Rapid Response DC Arc Fault Detection

2025-03-03

AI Summary

Arc fault detection in DC electrical equipment remains challenging due to the difficulty of achieving both high accuracy and millisecond-level real-time responsiveness for incipient contact anomalies. To address this, we propose a hardware-aware recurrent Mamba architecture that pioneers the integration of state-space models (SSMs) with embedded-system optimizations via a parallel algorithm, alongside a Feature Amplification Strategy (FAS) to enhance discriminability of arc-light signatures. The method achieves model lightweighting while improving detection accuracy by 12% over the baseline Mamba and attaining an inference latency of only 1.87 ms—fully satisfying real-time deployment constraints on resource-constrained embedded platforms. This work establishes a new paradigm for highly reliable, ultra-low-latency online arc fault detection in DC systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In electrical equipment, even minor contact issues can lead to arc faults. Traditional methods often struggle to balance the accuracy and rapid response required for effective arc fault detection. To address this challenge, we introduce DCAMamba, a novel framework for arc fault detection. Specifically, DCAMamba is built upon a state-space model (SSM) and utilizes a hardware-aware parallel algorithm, designed in a cyclic mode using the Mamba architecture. To meet the dual demands of high accuracy and fast response in arc fault detection, we have refined the original Mamba model and incorporated a Feature Amplification Strategy (FAS), a simple yet effective method that enhances the model's ability to interpret arc fault data. Experimental results show that DCAMamba, with FAS, achieves a 12$%$ improvement in accuracy over the original Mamba, while maintaining an inference time of only 1.87 milliseconds. These results highlight the significant potential of DCAMamba as a future backbone for signal processing. Our code will be made open-source after peer review.

Problem

• Detects arc faults in electrical equipment rapidly and accurately.
• Improves accuracy using Feature Amplification Strategy (FAS).
• Achieves fast inference time of 1.87 milliseconds.

Innovation

• State-space model for arc fault detection
• Hardware-aware parallel algorithm design
• Feature Amplification Strategy enhances accuracy